Analysis of Sparse Signal Sequences under Compressive Sampling Techniques for Different Measurement Matrices

Introduction: A more modern, extremely applicable method for signal acquisition is compression sensing. It permits effective data sampling at a rate that is significantly lower than what the Nyquist theorem suggests. Compressive sensing has a number of benefits, including a much-reduced demand for sensory devices, a smaller memory storage need, a greater data transfer rate, and significantly lower power usage. Compressive sensing has been employed in a variety of applications because of all these benefits. Neuro-signal acquisition is a domain in which compressive sensing has applications in the medical industry. Methods: The novel methods discussed in this article are FFT-based CoSaMP (FFTCoSaMP), DCT-based CoSaMP(DCTCoSaMP) and DWT-based CoSaMP (DWTCoSaMP) based on sparse signal sequences / dictionaries by means of Transform Techniques, where CoSaMP stands for Compressive Sampling Matching Pursuit with respect to Objective Quality Assessment Algorithms like PSNR, SSIM and RMSE, where CoSaMP stands for Compressive Sampling Matching Pursuit. Results: DWTCoSaMP is giving the PSNR values of 40.26 db, for DCTCoSaMP and FFTCoSaMP, PSNR is 36.76 db and 34.76 db. For DWTCoSaMP, SSIM value is 0.8164, and for DCTCoSaMP and FTCoSaMP, SSIM 0.719 and 0.5625 respectively. Finally, for DWTCoSaMP, RMSE value is 0.442, and for DCTCoSaMP and FFTCoSaMP, SSIM 0.44 and 0.4425, respectively. Conclusion: Among Compressed sampling techniques DWTCoSaMP, DCTCoSaMP and FFTCoSaMP discussed in this paper, DWTCoSaMP reveals the best results.